Modern applications of artificial intelligence and generally, machine learning appear to be driving innovations in robotics and specifically, in technologies involving autonomous robotics and autonomous vehicles. Also, the developments in machine perception technology have enabled the abilities of many of the implementations in the autonomous robotics' and autonomous vehicles' spaces to perceive vision, perceive hearing, and perceive touch among many other capabilities that allow machines to comprehend their environments.
The underlying perception technologies applied to these autonomous implementations include a number of advanced and capable sensors that often allow for a rich capture of environments surrounding the autonomous robots and/or autonomous vehicles. However, while many of these advanced and capable sensors may enable a robust capture of the physical environments of many autonomous implementations, the underlying processing circuitry that may function to process the various sensor signal data from the sensors often lack in corresponding robust processing capabilities sufficient to allow for high performance and real-time computing of the sensor signal data.
The underlying processing circuitry often include general purpose integrated circuits including central processing units (CPUs) and graphic processing units (GPU). In many applications, GPUs are implemented rather than CPUs because GPUs are capable of executing bulky or large amounts of computations relative to CPUs. However, the architectures of most GPUs are not optimized for handling many of the complex machine learning algorithms (e.g., neural network algorithms, etc.) used in machine perception technology. For instance, the autonomous vehicle space includes multiple perception processing needs that extend beyond merely recognizing vehicles and persons. Autonomous vehicles have been implemented with advanced sensor suites that provide a fusion of sensor data that enable route or path planning for autonomous vehicles. But, modern GPUs are not constructed for handling these additional high computation tasks.
At best, to enable a GPU or similar processing circuitry to handle additional sensor processing needs including path planning, sensor fusion, and the like, additional and/or disparate circuity may be assembled to a traditional GPU. This fragmented and piecemeal approach to handling the additional perception processing needs of robotics and autonomous machines results in a number of inefficiencies in performing computations including inefficiencies in sensor signal processing.
Accordingly, there is a need in the integrated circuitry field for an advanced integrated circuit that is capable of high performance and real-time processing and computing of routine and advanced sensor signals for enabling perception of robotics or any type or kind of perceptual machine.
The inventors of the inventions described in the present application have designed an integrated circuit architecture that allows for enhanced sensor data processing capabilities and have further discovered related methods for implementing the integrated circuit architecture for several purposes including for enabling perception of robotics and various machines.